QTL.gCIMapping.GUI Mapping small-effect and linked QTLs for complex traits in BC and F2

Introduction

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. Wen et al. (2019) Brief Bioinform 20(5): 1913-1924. Wang et al. (2016) Sci Rep 6: 29951. Zhang et al. (2019) Computational and Structural Biotechnology Journal 18: 59-65. Owing to the update of the R software, the QTL.gCIMapping.GUI software, rather than QTL.gCIMapping, fails in some functions. Thus, we updated new version of the QTL.gCIMapping.GUI software (2020-02-15). Please use the new version.

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Credits

  1. Yuan-Ming Zhang soyzhang@mail.hzau.edu.cn
    Investigator

    college of plant science and technology, Huazhong Agricultural University, China

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Summary
AccessionBT007078
Tool TypeApplication
CategoryQTL mapping, eQTL mapping
PlatformsLinux/Unix, MAC OS X, Windows
TechnologiesC++, R
User InterfaceDesktop GUI
Latest Release1.0 (November 10, 2019)
Download Count2190
Country/RegionChina
Submitted ByYuan-Ming Zhang
Fundings

The works were supported by the National Natural Science Foundation of China (31571268, 31871242, 31701071 and U1602261), Huazhong Agricultural University Scientific & Technological Self-innovation Foundation (Program No. 2014RC020), and State Key Laboratory of Cotton Biology Open Fund (CB2017B01 and CB2019B01).